code: def test_incremental_sparse_inputs():
      X = csr_matrix((3, 4))
      y = np.asarray([0, 1, 1], dtype=np.int32)
  
      X_da = da.from_array(X, chunks=(1, 4))
      y_da = da.from_array(y, chunks=(1))
  
      clf = SGDClassifier(tol=1e-3)
      wrap_clf = dask_ml.wrappers.Incremental(
          SGDClassifier(tol=1e-3),
          scoring="accuracy",
          assume_equal_chunks=True,
      )
  
      wrap_clf = wrap_clf.fit(X_da, y_da, classes=[0, 1])
      wrap_output = wrap_clf.predict(X_da).compute()
  
      clf = clf.fit(X, y)
      clf_output = clf.predict(X).astype(np.int64)
  
      assert_eq(clf_output, wrap_output, ignore_dtype=True)
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  import sklearn.model_selection
  from dask.array.utils import assert_eq
  from scipy.sparse import csr_matrix
  from sklearn.base import clone
  from sklearn.linear_model import SGDClassifier, SGDRegressor
  from sklearn.pipeline import make_pipeline
  import dask_ml.feature_extraction.text
  import dask_ml.metrics
  from dask_ml.metrics.scorer import check_scoring
  from dask_ml.wrappers import Incremental
label_desc: Use image node_id 12 for calling a global function with example usage: test_incremental_sparse_inputs() without return types